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Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective

Jiacheng Wang, Hongyang Du, Dusit Niyato, Jiawen Kang, Shuguang Cui, Xuemin Shen, Ping Zhang

arXiv:2310.01036v2eess.SP

TL;DR

ISAC requires methods that address communication and sensing challenges, including near-field DoA estimation when antenna spacing exceeds half the wavelength. This paper surveys GAI support across ISAC layers, focuses on physical-layer applications, and proposes a diffusion-model-based signal spectrum generator. In the case study, the method achieves a DoA estimation MSE of about 1.03 degrees.

  • Problem

    GAI integration into wireless systems remains limited, especially for XL-MIMO, near-field communications, and ISAC, while large antenna spacing creates phase ambiguity in near-field DoA estimation.

  • Method

    The paper reviews GAI and ISAC, analyzes GAI-enhanced physical-layer technologies, and proposes a diffusion-model-based signal spectrum generator for ambiguous near-field signal spectra.

  • Results

    About 1.03 degrees MSE is achieved for near-field DoA estimation when array spacing exceeds half the wavelength.

  • Takeaways & Limitations

    GAI-enhanced physical-layer technologies can support ISAC sensing and communication, with the case study addressing DoA estimation under large antenna spacing.

Abstract

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As generative artificial intelligence (GAI) models continue to evolve, their generative capabilities are increasingly enhanced and being used extensively in content generation. Beyond this, GAI also excels in data modeling and analysis, benefitting wireless communication systems. In this article, we investigate applications of GAI in the physical layer and analyze its support for integrated sensing and communications (ISAC) systems. Specifically, we first provide an overview of GAI and ISAC, touching on GAI's potential support across multiple layers of ISAC. We then concentrate on the physical layer, investigating GAI's applications from various perspectives thoroughly, such as channel estimation, and demonstrate the value of these GAI-enhanced physical layer technologies for ISAC systems. In the case study, the proposed diffusion model-based method effectively estimates the signal direction of arrival under the near-field condition based on the uniform linear array, when antenna spacing surpassing half the wavelength. With a mean square error of 1.03 degrees, it confirms GAI's support for the physical layer in near-field sensing and communications.

I. INTRODUCTION

The article examines how GAI can support ISAC, especially through physical-layer technologies, in response to challenges spanning wireless communication and sensing. It reviews cross-layer applications and demonstrates diffusion-model-based near-field DoA estimation when antenna spacing exceeds half the wavelength.

  • Motivation: GAI integration into emerging wireless systems remains limited, particularly for XL-MIMO, near-field communications, and ISAC.ISAC must balance sensing and communication demands for bandwidth, power, and other resources.
  • Motivation: Large antenna spacing can improve communication reliability but causes phase ambiguity in sensing-based DoA estimation.Communication favors independent signals across antennas, whereas sensing generally requires spacing no greater than half the wavelength.
  • Scope and contributions: The article investigates GAI applications in the physical layer and their potential support for ISAC from sensing and communication perspectives.It also reviews five GAI models and introduces a practical DoA-estimation use case.
  • Scope and contributions: The paper analyzes GAI enhancement of physical-layer technologies such as beamforming and signal detection, then examines their ISAC support and associated technical issues.The broader framework includes technologies such as CSI compression and signal detection for applications including indoor human detection and outdoor vehicle-to-vehicle communication.
  • Case study: 1.03 degrees MSE is achieved by the proposed signal spectrum generator for near-field DoA estimation with antenna spacing exceeding half the wavelength.The result is reported as evidence of the method’s effectiveness in addressing this sensing problem.

II. OVERVIEW OF GENERATIVE AI AND ISAC

The paper introduces GAI as models that learn complex data distributions and generate new data, then surveys five representative model families. It positions these models as applicable beyond content generation, including in wireless physical-layer technologies.

  • Generative AI: GAI models learn inherent patterns in large datasets and generate new data that is similar yet distinct from training data.Their stated advantage over traditional AI is the ability to capture complex, high-dimensional data distributions.
  • Generative AI: The overview covers GANs, normalizing flows, VAEs, diffusion models, and Transformers as representative GAI model families.The paper notes that these models support both digital-content generation and physical-layer wireless-communication applications.
  • Representative models: GANs use competing generator and discriminator networks, but reaching their training equilibrium is difficult.After training, the generator can produce similar yet new data in parallel.
  • Representative models: Normalizing flows map basic distributions to target spaces through reversible transformations, enabling likelihood estimation and complex-distribution sampling.The paper identifies potentially time-consuming training as a drawback when many transformations are required.
  • Representative models: VAEs compress and reconstruct data while modeling a latent distribution for dimension reduction, feature extraction, uncertainty estimation, and plausible-output generation.Their generated samples are not always interpretable because they derive from the latent space.
  • Representative models: Diffusion models learn generation by adding noise to training data and denoising it, while Transformers use self-attention to model long-range sequence dependencies.Diffusion inference requires many steps, whereas Transformers support parallel sequence processing and multimodal inputs.

B. Integrated Sensing and Communication

ISAC integrates wireless sensing and communication into a unified system to share limited resources. Its physical-layer designs include non-overlapping and overlapping arrangements with different resource-allocation and waveform choices.

  • ISAC concept: ISAC integrates wireless sensing and communication to use limited resources efficiently while supporting both functions.The system aims to combine the two functions rather than treating them as separate services.
  • Physical-layer classifications: Non-overlapping ISAC uses time-, frequency-, or space-division arrangements that assign distinct resources to sensing or communication.Time-division ISAC gives each task separate time slots, allowing preferred waveforms.
  • Physical-layer classifications: Overlapping ISAC designs are categorized as sensing-centric, communication-centric, or joint designs.
  • Benefits: Resource sharing in ISAC can increase wireless-network efficiency and reduce hardware and power-consumption costs.The cost reduction is associated with eliminating separate communication and sensing modules.
  • Benefits: ISAC can simultaneously meet communication requirements and provide sensing functions across varied application scenarios.The paper presents this versatility as a reason ISAC is considered a core technology for future 6G networks.

C. Potential Applications of GAI in ISAC Systems

GAI can support ISAC across the physical, network, and application layers. The paper links these layers to enhanced physical-layer performance, resource-management strategies, and communication or sensing data generation and analysis.

  • Layered support: GAI support for ISAC spans the physical, network, and application layers.
  • Physical layer: At the physical layer, GAI can support channel estimation, anomaly-signal identification, encoding, and beamforming.These technologies are described as improving communication performance and sensing accuracy.
  • Network layer: At the network layer, GAI can design resource-allocation strategies, scheduling schemes, and incentive mechanisms.The paper associates these uses with lower system cost and higher operational efficiency.
  • Application layer: At the application layer, GAI supports data generation, analysis, and feature extraction for communication and sensing applications.It can also generate substantial training data for communication and sensing models.
  • Cross-layer summary: Table I summarizes five typical GAI models and their potential support for ISAC at different layers.

III. GAI-ENHANCED PHYSICAL LAYER TECHNOLOGIES FOR ISAC

GAI strengthens ISAC physical-layer technologies by addressing signal detection, secure transceiver design, and other challenges across sensing and communication perspectives.

  • Physical-layer scope: The physical layer includes codebook design and channel estimation, which GAI can strengthen for ISAC sensing and communication.The section evaluates GAI-enhanced physical-layer technologies from both perspectives.
  • Signal detection: Normalizing unpredictable MIMO noise with neural fields enables signal detection without prior system-noise information.The framework uses unsupervised learning with only noise samples.
  • Secure transceivers: Variational autoencoders support secure transceiver pairs by managing codeword variation as transmission noise.The receiver loss includes a security term, and unsupervised training improves robustness against random codeword variations.

3) Sparse Code Multiple Access:

GAI-enhanced coding and compression methods support ISAC communication by improving robustness, reconstruction quality, and efficient CSI handling.

  • Sparse Code Multiple Access: GAN-based SCMA encoding and decoding improves noise immunity and BER performance for connected smart devices in ISAC.The encoder shortens information sequences and adds a noise layer, while PatchGAN and attention mechanisms reduce decoder complexity and improve BER.
  • Joint Source-Channel Coding: Nearly 3 dB higher average PSNR than traditional convolutional-neural-network methods is achieved by VAE-based JSCC.The VAE maps source data to a low-dimensional latent space and reconstructs it for transmission.
  • CSI compression: GAN-based CSI compression reduces high-dimensional channel state information for storage and transmission in sensing systems.The cited approach uses a CSiNet encoder at the transmitter to compress CSI into a low-dimensional representation.

2) Beamforming:

GAI models support ISAC beamforming, channel estimation, signal-parameter estimation, localization, and high-dimensional data processing through probabilistic modeling and restoration.

  • Beamforming: An 85% spectral-efficiency gain over exhaustive scanning of dominant beam pairs is achieved by a VAE-based adaptive beam-alignment framework.A DR-VAE models beam dynamics over long timescales, while short-timescale training uses feedback and probabilistic beam predictions.
  • Channel estimation: Diffusion models are suited to high-dimensional millimeter-wave MIMO channel estimation because they learn data-distribution gradients.The cited method trains a score-based generative model unsupervised on known channels.
  • Signal-parameter estimation: Converting low-SNR complex signals into images enables a GAN-based approach to improve signal-parameter estimation.The method uses a U-Net structure as the GAN generator.
  • Localization: Transformers capture inter-feature correlations among received signal-strength observations to boost multi-target localization.This is presented as an additional GAI application in sensing signal processing.
  • GAI capabilities: GAI captures complex data distributions, transforms data dimensions, and restores or enhances low-SNR or incomplete data for downstream ISAC processing.These capabilities support signal detection, beam prediction, compression, storage, transmission, and more precise parameter estimation.
  • Summary table: Table II organizes GAI use in physical-layer technologies and its potential support for ISAC communications by discussed, referenced, and unexplored areas.Blue cells represent discussed content, white cells reference other works, and empty cells denote unexplored areas.

IV. CASE STUDY

The case study uses diffusion models to address DoA-estimation ambiguity in near-field ISAC when antenna spacing exceeds half the wavelength.

  • Case study: Diffusion models address phase ambiguity in near-field DoA estimation when antenna spacing exceeds half the wavelength.The method targets signal-source localization and can also facilitate active near-field communication beamforming.

A. Problem Description

Near-field DoA estimation becomes ambiguous when antenna spacing grows, and the proposed diffusion-based SSG reconstructs a clear signal spectrum from ambiguous observations. Experiments show accurate spectrum generation, DoA estimation, and improved localization.

  • Problem Description: When antenna spacing expands to λ, increased propagation path length creates phase ambiguity that prevents reliable identification of the true near-field DoA.For a ULA, DoA is inferred from phase differences across adjacent antennas; spacing at or below 0.25λ supports a one-to-one correspondence.
  • Proposed Design: The diffusion-based SSG learns the relationship between ambiguous and clear signal spectra by denoising toward an expert solution.Ambiguous spectra serve as observations, while correct spectra provide training targets for the forward and backward diffusion process.
  • Performance Evaluation: Test reward stabilizes around -10 for SSG versus -80 for the DRL-based method, indicating better signal-spectrum reconstruction.The difference between generated and expert solutions narrows during training as the denoising network learns its hyperparameters.
  • Performance Evaluation: 1.03 degrees is the reported DoA estimation MSE for the generated spectrum against ground truth.The generated DoAs of 31, 99, and 146 degrees closely align with expert values of 30, 99, and 146 degrees.
  • Performance Evaluation: 0.21λ is the median localization error with SSG, compared with 1.25λ without SSG.The evaluation assumes accurately estimated range and uses the three highest-amplitude DoA peaks for localization.

V. FUTURE DIRECTIONS

GAI introduces security risks in physical-layer ISAC applications, including attacks that can disrupt training or impair model-based communication functions.

  • GAI Application Security: Training-dataset attacks can cause GAI model non-convergence or failure, while attacks on the model can impair channel estimation and coding.The paper identifies dataset and model security as future research priorities and mentions blockchain as one possible approach.

B. Resource Allocation

Deploying GAI in the physical layer requires managing its computational, storage, and communication resource demands. The paper frames GAI-enhanced physical-layer processing as support for ISAC while highlighting resource reallocation needs.

  • Resource Allocation: GAI training and operation consume computational, storage, and communication resources, disrupting the original system’s resource balance.Integrating GAI into the physical layer therefore requires resource reallocation for stable operation.
  • Resource Allocation: When local resources are abundant, resource strategies should maximize benefits while minimizing consumption according to task complexity and real-time requirements.The paper distinguishes resource-management strategies by local resource availability.
  • Resource Allocation: When local resources are constrained, dynamic spectrum access is proposed as an incentivization mechanism to preserve functional effectiveness.The stated objective is to maximize benefits under constrained local resources.
  • GAI Support for ISAC: GAI-enhanced physical-layer technologies can potentially support ISAC across sensing and communication aspects, including the diffusion-based SSG case study.The article concludes that physical-layer applications primarily use GAI for complex data feature extraction, transformation, and enhancement.
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